Gender Clustering of Blog Posts using Distinguishable Features
Yaakov HaCohen-Kerner, Yarden Tzach and Ori Asis
Department of Computer Science, Jerusalem College of Technology (Machon Lev),
21 Havaad Haleumi St., P.O.B. 16031, 9116001 Jerusalem, Israel
Keywords: Blog Posts, Distinguishable Features, Gender Clustering.
Abstract: The aim of this research is to find out how to perform effective clustering of unlabeled personal blog posts
written in English by gender. Given a gender-labeled blog corpus and a blog corpus that is not gender-
labeled, we extracted from the labeled corpus distinguishable unigrams for both males and females. Then,
we defined two general features that represent the relative frequencies of the distinguishable males’
unigrams and females’ unigrams, (males’ frequency and females’ frequency). The best distinguishable
feature was found to be the males’ frequency feature with a ratio factor at least 1.4 times that of females.
This feature leads to accuracy rate of 83.7% for gender clustering of the unlabeled blog corpus. To the best
of our knowledge, this study presents two novelties: (1) this is the first study to cluster blog posts by gender,
and (2) clustering of an unlabeled corpus using distinguishable features that were extracted from a labeled
corpus.
1 INTRODUCTION
Due to the explosion of information on the Internet
and its increased availability there is a need to
automatically perform text classification. However,
most of the texts are not pre-assigned to classes, and
therefore they are unsuitable for supervised machine
learning (ML). Hence, document clustering using
unsupervised ML is necessary.
Clustering is an automatic grouping of unlabeled
text documents into groups, which are called
clusters. Clustering of documents is the process of
creating a set of clusters in such a way that
documents within one cluster are more similar and
documents from different clusters are more
dissimilar (Tryon, 1939; Bailey, 1994). Clustering is
applied in various domains including bioinformatics
(Tasoulis et al., 2004), data mining (Fayyad et al.,
1996), genetics (Shamir and Sharan, 2000), machine
vision (Cucchiara, 1998), and social sciences
(Aldenderfer and Blashfield, 1984).
The research presented in this paper was
performed in the blog domain. “Blog is a popular
and flexible way to publish information and express
feelings, especially for private use” (Gao and Lai,
2010). The selected application domain is blog posts
clustering by gender. The motivation for gender
classification and clustering has grown during the
last years, with rise of the digital age and the
increase in human-computer interaction (Ngan and
Grother, 2015).
Furthermore, the language used by an author is
impacted by variables such as the author's age and
gender (Eckert, 1997; Eckert and McConnell-Ginet,
2013). Bucholtz and Hall (2005) showed that
speakers use language as a resource to construct
their identity. In many cases, the person’s gender
identity can be identified by finding the linguistic
features associated with male or female speech.
These linguistic features gain social meaning in a
cultural and societal context. On Twitter, for
instance, users construct their identity through inter-
acting with other users (Marwick and Boyd, 2011).
The main problem in the gender clustering task
is identifying the clustering properties, which can be
clearly distinguished from one cluster to another and
decide how clusters should be defined. The task of
clustering by gender is sometimes difficult even for
a person to perform. The gender of the author of a
blog can be conjectured based on the subjects
discussed or its writing style (Schler et al., 2006;
Schwartz et al., 2013). However, even then there is
no promise for perfect identification in all cases.
Sometimes females write about topics that are
considered as masculine subjects (e.g., computers,
electronics, and politics) or use male writing style
384
HaCohen-Kerner, Y., Tzach, Y. and Asis, O.
Gender Clustering of Blog Posts using Distinguishable Features.
DOI: 10.5220/0006077403840391
In Proceedings of the 8th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2016) - Volume 1: KDIR, pages 384-391
ISBN: 978-989-758-203-5
Copyright
c
 2016 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved

and sometimes males write about topics that are
considered as feminine subjects (e.g., home, family,
and feelings) or use female writing style (Schler et
al., 2006). One of the most basic methods to
differentiate between males and females is to have
two word lists so that one list includes words that are
relatively more common for males and the second
list includes words that are relatively more common
for females.
We worked with two corppra that are of the
same type, personal blogs written in English. The
first corpus is from August 2004 and the second
corpus is from 2012. The blogs of the first corpus
are already gender-labeled while the blogs of the
second corpus are not gender-labeled.
To the best of our knowledge, our research is the
first to cluster blog posts by gender. Blog clustering
by gender is important due to the huge number of
unlabeled texts, which is available in the Web in
general and in blogs in particular. Automatic gender
clustering will enable people and companies to learn
new things about females versus males and to take
advantage of this knowledge (e.g., for marketing
purposes).
This study claims that blog posts clustering using
distinguishable features extracted from a labeled
corpus lead to better clustering results for another
similar corpus, which is not labeled. That is to say,
we efficiently perform gender clustering on an
unlabeled corpus using features extracted from a
corpus, which is labeled. We did not see any use of
such distinguishable features in former relevant
clustering studies.
The main contribution of this study is the
presentation of distinguishable unigrams that were
extracted from a labeled blog corpus and were found
as successful features for clustering of a totally
different and unlabeled blog corpus from the same
domain, personal blog posts written in English. We
found that males distinguishable unigrams with a
ratio factor at least 1.4 times that of females lead to
an accuracy rate of 83.7% for gender clustering. The
use of other features (from other feature sets and/or
from the same feature set) harmed the clustering
results.
This paper is organized as follows: Section 2
gives an overview for a much related domain,
gender classification, Section 3 describes blog
clustering and suitable feature sets. Section 4
introduces the selected features for this research.
Section 5 presents the clustering model. Section 6
describes the experimental results and their analysis.
Finally, Section 7 presents a summary and proposals
for research directions.
2 GENDER CLASSIFICATION
A related domain to gender clustering is gender
classification. Gender classification in Natural
Language Processing (NLP) is the supervised
learning task of assigning natural language text
documents to males or females according to their
content. Author gender identification has been
studied both as an authorship attribution task and
gender classification task (Koppel et al., 2002).
Koppel et al. (2002) showed that automated text
classification techniques can exploit combinations of
simple lexical and syntactic features to infer the
gender of the author of an unseen formal written
documents (writing styles in modern English books
and articles). The best accuracy results (around
80%) have been obtained when using both function
words and parts-of-speech n-grams.
Yan and Yan (2006) constructed a corpus
containing 75,000 individual blog entries authored
by 3000 bloggers. They also presented a Naïve
Bayes classification approach to identify genders of
weblog authors. In addition to features employed in
traditional text classification, the authors used
weblog-specific features, e.g., web page background
colors and emoticons. They presented the most
“gender-discriminant” unigrams that they have
found, e.g., “hit”, “man”, “peace”, “played”, and
“yo”.
Mukherjee and Liu (2010) proposed two novel
methods to improve the state-of-the-art accuracy
results. Their first method introduces a new class of
features, which are variable length POS sequence
patterns mined from the training data using a
sequence pattern mining algorithm. The second
method is a new feature selection method, which is
based on an ensemble of several feature selection
criteria and approaches. Empirical evaluation using a
real-life blog data set shows that these two methods
significantly improve the classification accuracy of
the current state-of-the-art methods.
Burger et al. (2011) constructed a large,
multilingual dataset labeled with gender and
presented a few configurations of a language-
independent classifier for identifying the gender of
Twitter users. The best classifier performed at 92%
accuracy.
Filho et al. (2016) performed gender
classification by using 60 textual meta-attributes for
the extraction of gender expression linguistic cues in
tweets written in Portuguese. The authors take into
account a widespread variety of features: characters,
syntax, words, structure and morphology. They
classified free texts posted on Twitter according to
Gender Clustering of Blog Posts using Distinguishable Features
385

author’s gender using three different ML algorithms
as well as evaluate the influence of the proposed
meta-attributes in this process.
3 BLOG CLUSTERING AND
SUITABLE FEATURE SETS
As mentioned in Section 1, clustering is an
automatic grouping of unlabeled text documents into
groups, which are called clusters. In general, text
clustering is much less popular than text
classification (can be proved by numbers of papers
and citations), which is an automatic grouping of
labeled text documents into groups. There are
significantly much more studies about text
classification than about text clustering. The same
(clustering versus classification) is true for gender
and/or blog tasks. Probably, the main reasons for
these findings are that: (1) text clustering is much
harder to evaluate than text classification and (2)
there are much less clustering (unsupervised ML)
methods compared to supervised ML methods.
Therefore, the accuracy results achieved by text
clustering tasks are usually significantly lower than
the results achieved by text classification tasks.
Clustering of blogs based on similar content
using blog tags was performed by Brooks and
Montanez (2006). They used the top 350 blog tags
and they found that the tags are useful for clustering
of articles into broad clusters, but less effective in
indicating the particular content of an article. They
showed that automatic extraction of words deemed
to be highly relevant lead to better categorization of
articles. Kuzar and Navrat (2011) present Slovak
blog clustering enhanced by comments of web users.
They combined content clustering with implicit ties
between users based on comments. According to the
results of their experiments, the quality of content
clusters can be improved by considering implicit ties
between commentators in case of articles, which do
not fit into a single cluster.
Various feature sets have been applied in
clustering tasks. Miao et al. (2005) applied three
types of feature sets for document clustering: (1)
words (after removing stopwords, stemming,
pruning rare terms, and tf-idf weighting), (2) terms
(based on their C Value, i.e., a frequency-based
weight that accounts for nested terms), and (3)
frequent character n-grams. They found that the n-
gram-based representation provides the best results.
Banerjeeet al. (2007) introduced a method of
improving the accuracy of clustering short texts by
enriching their representation with additional
features from Wikipedia. Empirical results showed
that their enriched representation of text items
substantially improved the clustering accuracy when
compared to the conventional bag of words
representation. HaCohen-Kerner and Margaliot
(2013; 2014) proposed for clustering various types
of word unigrams, e.g., most frequent words (FW)
including function words (stopwords), most frequent
filtered words (FFW) excluding function words, and
words with the highest variance values (HVW).
Nguyen et al. (2014) explained why it is hard to
predict gender and age from tweets. They showed
that most research so far treats gender and age as
fixed variables and ignores that language use is
related to the social identity of speakers, which may
be different from their biological identity. In their
research, they showed that approaching age and
gender as social variables allow for richer analyses
and more robust systems.
4 SELECTED FEATURES
In this research, we consider until now features
belong to only two feature sets: 45 Parts of Speech
(PoS) features and 2 distinguishable features (D) as
follows. The 45 PoS features were produced using
the Stanford Part-of-Speech tagger
(http://nlp.stanford.edu/software/tagger.shtml)
(Toutanova et al., 2003). We normalized each one of
the PoS features by the number of tokens of the post.
The D features were created from the labeled
corpus (C1) as follows. Separately, for each sub-
corpus (males, females) we activated the following
process: The frequency of each unigram (word) in
the corpus is counted (including stopwords). Only
the unigrams with a frequency of at least 100
occurrences in the corpus are selected. For each
sublist (males, females) only unigrams that appear in
one sublist at least 1.2 times more than in the second
sublist are selected as distinguishable unigrams.
These distinguishable unigrams are sorted in non-
ascending order, separately for each sublist. In this
way, we obtained two long lists each of which
contained several thousand words. For instance, for
ratio factors of 2 or above (2+), the list of male
unigrams consisted of 3401 words, and the list of
female unigrams consisted of 2114 words. Table 1
introduces a few examples from these two lists
generated from C1.
KDIR 2016 - 8th International Conference on Knowledge Discovery and Information Retrieval
386

Table 1: Examples of distinguishable unigrams from the
two lists with ratio factor of 2+.
Distinguishable males’
unigrams
Distinguishable females’
unigrams
scientists, criticism, war,
bush, US, UN, Israel,
terrorists, Linux, PC,
Microsoft, networks,
xbox, NBA, eBay, Greece,
Sony, URL, beta, java,
explorer, servers,
government, businesses,
SQL, rural, economics,
constitutional, tactics,
stance, versus, presidents,
samurai, IP, mankind,
enterprise, batman, FBI,
civilians, GOP, veterans,
CBS, NFL, Firefox, XML,
electoral, IBM, .NET,
NBC, commands
mom, baby, cuz, wedding,
boyfriend, laundry, ugh,
*sigh*, pregnant, jeans,
yummy, butter, OMG,
skirt, dishes, freaked,
kisses, knitting, kitty,
purse, makeup, sweater,
outfit, bf, recipe, heels,
photographer, dresses,
massage, dork, oven,
girly, nursing, babysitting,
scarf, bake, flirt, comfy,
feminist, LMAO, crushes,
tanning, lipstick,
brownies, muffin, witches,
broccoli
We did not use these thousands of words as
classical features, where each unigram is a feature.
However, we applied and used only two features for
each post. These two features represent the relative
frequencies of the distinguishable males’ unigrams /
females’ unigrams, i.e., the number of
distinguishable males’ unigrams / females’ unigrams
in a post normalized with respect to the total number
of words in a post. We call these features: males’
frequency and females’ frequency, respectively.
5 THE CLUSTERING MODEL
Blog post clustering presents challenges due to the
large number of potential features available, their
dependencies, and the large number of training
posts. Appropriate corpus construction, feature
selection and text clustering are critical to the
success of the clustering tasks.
We used two free available corpora. The first
corpus (we call it C1) contains over 71,000 blogs
including 681,288 blog posts with more than 140
million words (Schler et al., 2006). These blog posts
were downloaded from Blogger.com one day in
August 2004. Of all the bloggers: 37,324 are males
and 34,169 are females. The C1 corpus is labeled.
That is to say, for each post we know the gender of
its author. Schler et al. (2006) performed gender
classification tasks. They used 502 stylistic features
(POS tags, blog words and hyperlinks) and 1000
unigrams with the highest information gain in the
training set. They obtained an accuracy of 80.1%.
The second corpus (we call it C2) was published
and distributed to the public by ICWSM
(http://www.icwsm.org/2012/home/media/)
(International AAAI Conference on Web and Social
Media) in 2012. The C2’s posts were downloaded
from various blog web-sites, e.g., Blogger.com,
WordPress, and LiveJournal. The C2 corpus is
unlabeled. That is to say, for each post we do not
know the gender of its author.
In view of the fact that the computers that were
available to us were "modest" PCs and the run time
was very long, it was decided to significantly “cut”
the corpora and to base them only on posts (one for
each selected blogger). Moreover, C2 included many
noisy posts relatively to our clustering task, e.g.,
posts that were not written in English, posts
containing sequence(s) of characters and symbols
that are lack of context, and posts containing
commercial ads.
C1 was filtered by us as follows. Firstly, we
downloaded the 19,320 blogs that were chosen by
Schler et al. (2006) for their classification task. For
each one of these blogs, we selected only the first
post. We received a new corpus including 19,320
posts. The filtered corpus was called C1B.
Concerning C2, due to the relatively high percentage
of noise data, we had to perform a long and slow
manual filtering process. As a result, we received a
relatively small-sized corpus containing 1,001 posts.
This corpus is called C2B. Then, we cleaned each
corpus by deletion of redundant spaces and
normalization of words (e.g., transformation of
letters from uppercase to lowercase and deletion of
punctuation marks, e.g., ‘,’, ‘;’, ‘.’, ‘!’, ‘?’, ‘’’, and
‘”’).
5.1 Selected Clustering Method
There is a wide range of clustering methods.
Clustering methods can be divided into non-
hierarchical methods such as K-means (Steinhaus,
1956) and K-means improved variants such as
(Dhillon et al., 2002; Kanungo et al., 2002) and
Expectation Maximization (EM) (Dempster et. al,
1977) and its improved variants such as (Bradley et
al. 1998; Frenkel and Feder, 1999), and hierarchical
methods such as hierarchical clustering (Johnson,
1967). Comprehensive surveys of various clustering
methods and/or applications are for example: Jain et
al. (1991), Zheng et al. (2006), and Aggarwal and
Zhai (2012).
There is no agreed definition about what is a
"good" or "correct" clustering method or as it was
written by Estivill-Castro (2002) "clustering is in the
Gender Clustering of Blog Posts using Distinguishable Features
387

eye of the beholder". We decided to apply a
commonly used cluster algorithm, which is called
the expectation–maximization (EM) algorithm
(Dempster et. al, 1977). This method uses an
iterative computation of maximum-likelihood
estimates. Each algorithm's iteration consists of an
expectation step followed by a maximization step.
This method assumes that the desired clusters have a
normal distribution. The EM method with its default
parameters has been applied using the Waikato
Environment for Knowledge Analysis (WEKA)
platform (Witten and Frank, 2005; Hall et al., 2009).
5.2 Feature Filtering
We decided to use the correlation feature selection
(CFS) filtering method (Hall, 1999; Senliol et al.,
2008) with its default parameters. CFS is the default
filtering method implemented in WEKA. “CFS
(Correlation-based Feature Selection) assumes that
useful feature subsets contain features that are
predictive of the class but uncorrelated with one
another. CFS computes a heuristic measure of the
“merit” of a feature subset from pair-wise feature
correlations and a formula adapted from test theory.
Heuristic search is used to traverse the space of
feature subsets in reasonable time; the subset with
the highest merit found during the search is
reported.” (Hall, 1999, p. 74).
5.3 Measurement of Results
For each experiment, we randomly choose 100 posts
and for each post we checked whether the clustering
choice was correct. The check was done by a mother
tongue English speaker who read every word in the
post and decided whether the author was a man or a
woman. The accuracy rate for each experiment was
defined according to the accuracy rate for the sample
of 100 posts.
The number of clusters that is created by the EM
method does not always match the number of groups
that we intend to cluster. Often the number of the
resulting clusters is higher. For instance, assuming
clustering by gender, we can get five clusters while
we expect only to two (males and females). When
we look at the visual presentation of the resulting
clusters, two clusters might be located on one side of
the graph, the area that represents high values of
feminine characteristics, while the other area
contains the other three clusters representing high
values of masculine characteristics. In this case, the
two first clusters are defined as clusters of females,
and the rest as clusters of males.
6 EXPERIMENTAL RESULTS
In this section, we present and analyze the clustering
experiments of the personal blog posts included in
the C2B corpus by gender using the EM clustering
method, the CFS filtering method, and the PoS
features for the C1B corpus and the D features
extracted from C1B, as explained before.
In experiment # 1, we apply the CFS filtering
method on the PoS features, and we get only 2
distinguishable features: DT (determiners) and PRP
(pronouns). While DT consists of articles and is,
therefore, more associated with males, PRP
represents pronouns and is, therefore, more
associated with females (Schwartz et al., 2013). The
clustering accuracy result was only 59.78%. In
experiment # 2, we did not apply the CFS filtering
method. We applied EM on 4 features: DT, PRP and
the two D features. The clustering accuracy result
was slightly higher 60.4%. In experiment # 3, we
applied the CFS filtering method on the PoS and the
D features and we get only 12 distinguishable
features: 11 PoS features and the females’ frequency
feature. The clustering accuracy result was 67.33%.
Table 2 and Figure 1 present 10 additional
experiments where the accuracy of the gender
clustering is presented as a function of either one D
feature (males’ frequency) or of the two D features.
The results using only the females’ frequency
feature are not presented because they were
significantly lower. A possible explanation to these
findings is that the list of the males’ distinguishable
unigrams is much longer than the list of the females’
distinguishable unigrams, as can be seen for ratio
factor of at least 2, where the list of male unigrams
consisted of 3,401 words while the list of female
unigrams consisted of only 2,114 words. This could
be because females write in a more consistent and
straightforward way than males (Schwartz et al.,
2013), which leads males to use a larger variety of
words than females.
Table 2: Accuracy results according to the ratio factor.
2+
1.8+ 1.6+ 1.4+ 1.2+ Ratio factor
69.3 72.8372.83 83.7 71.74
Males’
freq.
Accuracy
68 70.6570.65 80.43 70.65
Males’ &
females’
freq.
A few interesting conclusions can be drawn from
the gender clustering experiments: (1) the males’
frequency feature was found as the best clustering
feature, (2) among the various values tested for the
KDIR 2016 - 8th International Conference on Knowledge Discovery and Information Retrieval
388

ratio factor for the males’ frequency feature, the
optimized accuracy result (83.7%) was obtained by a
ratio factor of 1.4+. Increase or decrease in this ratio
factor leads to lower results, and (3) using both the
males’ and the females’ frequency features with the
same values of the ratio factors lead to a curve
similar to the curve that is based on the males’
frequency feature only but with lower values.
Figure 1: Accuracy of the gender clustering model as a
function of the ratio factor of two types of features.
In our experiments, the CFS filtering method did
not select the features that led to the best accuracy
results. Strict manual selection and removal of
ineffective features done by trial and error was much
more successful.
As mentioned before, to the best of our
knowledge, our research is the first to cluster blog
posts by gender. Therefore, we cannot compare our
method with state-of-the-art methods. A very partial
comparison can be made with the gender
classification study on the C1 corpus (Schler et al.,
2006) achieving an accuracy of 80.1%. Our
clustering method achieves a slightly better accuracy
result (83.7%) indeed on another corpus (C2B).
Furthermore, our result is quite promising because it
is known that clustering is regarded as a difficult
task than classification.
7 SUMMARY AND FUTURE
WORK
In this paper, we propose a methodology for
effective clustering of personal blog posts written in
English by gender. To the best of our knowledge, we
present two novelties: (1) this is the first study to
cluster blog posts by gender, and (2) the use of
distinguishable features that were extracted from a
labeled corpus for the clustering of another similar
corpus, which is not labeled.
We constructed two filtered corpora from two
free available personal blog corpora. Each blog from
the first corpus was already gender-labeled while the
blogs of the second corpus were not labeled. We
extracted from the labeled corpus distinguishable
unigrams for both males and females. Then, we
defined two features that represent the relative
frequencies of the distinguishable males’ unigrams
and females’ unigrams, (males’ and females’
frequency). The best distinguishable feature was
found to be the males’ frequency feature with a ratio
factor of 1.4 relatively to females. This feature leads
to an accuracy rate of 83.7% for the gender
clustering task of a totally different blog corpus,
which is unlabeled.
There is much room for future research in this
domain. Possible directions are: (1) Applying
additional feature sets for the clustering tasks, e.g.,
N-grams (N > 2), function words, orthographic
features, quantitative features, and topographic
features. These features can help with style-based
clustering and might help detect differences in style
that are characteristic of the gender of the author as
well as with other tasks; (2) Conducting additional
experiments using much larger unlabeled blog posts
corpora and additional combinations of ratio factor
and minimal frequencies of distinguishable N-
grams; (3) Comparing between the proposed method
and other methods such as (a) gender-based
supervised classification, and (b) simple document
clustering exploiting, e.g., textual similarity; and (4)
Extending the experiments to other interesting
clustering tasks such as clustering by age,
personality type, political orientation, and regional
origin.
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